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Great thread by @EmilDimanchev here!
Funny thing about path dependency: within certain physical and economic limits, we can *choose* the energy system we want, and then unit costs will come down thanks to learning.
Dorky version: choose a local minima for our non-linear world. https://twitter.com/EmilDimanchev/status/1315619195904720898">https://twitter.com/EmilDiman...
Funny thing about path dependency: within certain physical and economic limits, we can *choose* the energy system we want, and then unit costs will come down thanks to learning.
Dorky version: choose a local minima for our non-linear world. https://twitter.com/EmilDimanchev/status/1315619195904720898">https://twitter.com/EmilDiman...
There were already well-thought-out concepts for renewable energy systems in the 1970s, but the technologies didn& #39;t receive enough support early enough. https://twitter.com/nworbmot/status/1142758825520447488">https://twitter.com/nworbmot/...
The Danes pushed wind power down the learning curve in the 80s and 90s (see "Quitting Carbon" by @JustinGerdes), and several countries did the same for PV over several decades (see "How Solar Got Cheap" by @GregNemet), but we could have done it earlier. https://www.howsolargotcheap.com/ ">https://www.howsolargotcheap.com/">...
I don& #39;t doubt that a concerted state-led push for nuclear reactors with wide public support, with a follow-up programme for fast breeders, could have pushed us into a nuclear-dominated equilibrium.
And as @EmilDimanchev points out, we happen to be in crappy fossil-fuelled equilibrium right now, which isn& #39;t really optimal on counts that many think are important (air quality, climate, equity, etc.).
And just because a technology is foundering or "non-commercial" right now, doesn& #39;t mean that it shouldn& #39;t be supported from the multi-decadal perspective of pushing us onto a new pathway of our choice.
There& #39;s a nice demonstration of multiple local optima in models with learning in Niclas Mattsson& #39;s 1997 licentiate thesis, where he introduced the MILP model of learning curves that was later picked up in several IAMs, see his doctoral thesis summary: https://research.chalmers.se/en/publication/514513">https://research.chalmers.se/en/public...
By killing off some branches of the branch-and-bound algorithm using temporary constraints, he could force it down particular paths.
This is a neat trick, since if you& #39;re working in a nonlinear system, rather than the piecewise MILP linearisation, it can be hard to find all local minima (e.g. you can try lots of different starting points, simulated annealing etc.).
